Acceptance in Blame: How and why we Blame the Victims of Street Harassment
Bibliographic record
Abstract
Globally, and on a daily basis, women are subjected to unwanted verbal and/or physical intrusions such as catcalling, leering, honking, sexually explicit or sexist comments, touching or grabbing, amongst other actions that are all considered street harassment. This paper is a review of some of the literature available, which focuses on the psychological and feminist aspects of street harassment and victim blaming through social, cognitive, intersectional, and economic lenses. Regarding psychological theories, I will examine reasons why victim blaming happens through the theories of the just-world hypothesis, cognitive dissonance, and the bystander effect. The feminist theories touch on the basics of objectification and power dynamics found within gender, which can help us understand why street harassment happens. Lastly, I will emphasize the importance of starting a conversation about the pervasiveness of street harassment and victim blaming, and why it is important to know where the blame should be instead of where it is almost always placed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".